Eye movement patterns, including the pupillary response, have been shown to correlate with cognitive states such as mental workload or time-on-task fatigue. Likewise, these signals have been shown to directly correlate with behavior in both simple and complex tasks. In this study we explored the link between eye movement patterns and performance in a simulated guard duty task. Here, subjects viewed a sequence of photographs of individuals along with their corresponding identification card (ID) to determine the validity of each ID. The number of IDs awaiting verification randomly fluctuated during the task and was visually represented to the subject in the form of a dynamic queue to vary task load. Behavioral performance, EEG, gaze position and pupil diameter were measured continuously throughout the duration of the task. Using an established saccade detection algorithm, we were able to generate a number of features from the eye movement data. Through regression analysis, we found that both the length of the queue and reaction time were significantly correlated with eye movement features, including pupil diameter, saccade and blink frequency. In a similar fashion, we used Independent Component Analysis (ICA), combined with linear regression, to identify the EEG features most predictive of behavioral performance. Interestingly, a number of the most predictive EEG features were also eye movement related. In line with previous studies, our results demonstrate that a significant amount of task-relevant information can be extracted from patterns of eye movements.